PROJECT SUMMARY Age is the greatest risk factor for Alzheimer?s disease (AD). Nevertheless, a majority of individuals do not develop AD by their 9th decade of life. We propose that targeting the endogenous protective mechanisms associated with ?healthy aging? represents a sound scientific paradigm for addressing AD prevention, especially if this can be achieved using a combination of pre-existing FDA approved drugs. Our initial models of human aging identified novel transcriptomic signatures of aging, common in human hippocampus and muscle, prompting us to develop an expanded biobank and an updated assay that integrates both protein coding and long noncoding RNA (lncRNA) data using novel RNA quantification methods, and extensively phenotyped clinical resources. We have utilized this translational bioinformatics strategy and a novel human biobank to produce a robust prototype screen that successfully identified >100 drugs from in silico analysis using the NIH cMAP/LINCS database, including 29 chemical regulators of a proven longevity pathway (PI3K/mTOR, p<0.00001). We validated this activity in human primary cells. Here, our overarching goal is to refine this screen in a range of human cell models and identify optimal combinations of FDA approved drugs that positively regulate the healthy-aging-related RNA network without the focused need for direct PI3K/mTOR inhibition and hence reduced potential side effects. Through the pursuit of 3 major distinct but interacting aims, the present body of work will enhance the network modeling by incorporating human brain aging lncRNA responses; formatting the current validated assay into a higher-throughput screen able to examine ~5,000-10,000 combinations of individually active drugs. We expect to demonstrate that our strategy can guide repositioning of individual drugs into sets of drug-combinations that ?switch on? a healthy-aging profile in human cell systems.